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Dynamical Networks for Agent Systems

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The basic ideas for a novel class of recurrent neural networks that learn through active interaction with an unknown environment by reading data and reward information and by emitting actions through its I/O interface. The network’s connections are fixed and no weights need to be adjusted. A neuron’s activity is determined by one of two possible boolean gates. Starting out with a uniform distribution over the two gates, learning consists in assigning a gate to each neuron as evidence is gathered, thus “programming” the network on-the-fly.

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